By Bernadette Howlett, PhD, Director of Faculty Operations Research

WGU is thrilled to announce that it was one of 50 winners chosen from nearly 2000 competitors in the 2023-24 Tools Competition, administered by The Learning Agency and Georgia State University. Winners will share in an investment award of more than $8 million, which will be used to transform education for more than 24 million learners of all ages worldwide. 

Imagine 500 seven-year-olds lined up on a sunny, tree-lined street in front of you with their shiny bicycles and eager smiles beneath cartoon character helmets, ready to learn to ride a bike for the first time. You, their singular instructor, have prepared a master bicycle-riding curriculum with vibrant learning materials to show your students. How will they fare? How will you fare as their teacher? 

Likely there would be considerably more skinned knees, tears, and feelings of failure than kids pedaling down the street for the first time. Even more likely, many children would not learn to ride that day, and might well never learn to ride, due to the negative experience of trying to learn in a less-than-optimal teaching situation. Some of the children may spend their lives believing they are unable to ride a bike and unwilling to ever try again. As for you, the sole teacher to 500 children, tending to injuries and fending off parental lawsuits might well be your outcome!

There is a reason most children learn to ride a bike in a one-on-one (or two-adult-to-one-child) setting. The reason is so obvious, in fact, that this bicycle-riding lecture scenario is easily identifiable as preposterous without any supporting evidence or theoretical frameworks. In higher education for perhaps the last two centuries (and particularly since the GI Bill), we have relied on the efficiencies of large ratios of students to faculty members... even more so in online learning. But in an increasingly tech-enabled learning landscape, we have the flexibility to step back and ask—is this really the best way for students to learn? 

A Little History of Human Learning

Before higher education adopted the current industrial model of education (bringing students from farms and villages to cities and into large lecture halls), teaching and learning was a more personal experience, often with a master who had a small number of apprentices, or even just one. That model existed for most of human history and could be argued to be the optimal way to learn, just as we teach children to ride bikes in one-to-one situations.  

Of course, the specific competencies to be taught contribute to the design of instruction, driving the choice of allowing a faculty member to have a course with 500 students per session (the classic lecture course) verses assigning a handful of students to a faculty member (as in a clinical rotation or capstone course). However, the efficiencies of the industrial model have shown significant wear over recent decades, leading to serious questions about the value proposition of a college education.

The central issue caused by the move away from individualized, high-touch to low-touch learning is that the industrialized model of education has exceeded its zenith for efficiency. Students feel increasingly disconnected in an era when connection in some form is nearly ubiquitous. The fields of adult and higher education need to personally connect with each student without increasing costs, and to do so in a manner that meets the unique needs of the student each day – to leverage the master/apprentice model at scale. 

WGU Leads Innovation in Higher Education

In the last two years, WGU has won two national awards for innovation (Encoura, 2023; The Tools Competition, 2024). The Tools Competition award just received by WGU is for the revolutionary idea of combining a paradigm popular in manufacturing, retail, and other industries called Decision Intelligence (DI) with our best-in-class educational model. WGU students get one-to-one support from two faculty members: a mentor with professional expertise in the student’s field of study, who guides the student through their degree program; and an instructor with deep subject-matter expertise, who facilitates the student’s achievement of course competencies.  

This student/mentor/instructor model is limited by manual tasks performed by faculty members one student at a time. This use of labor has led to more faculty time being spent on administrative functions, limiting time available to interact directly with students. WGU is increasing student/faculty interaction by combining the excellence of master/apprentice learning with the genius of artificial intelligence (AI), creating a human-in-the-loop solution to deliver personalized learning at scale.

Decision Intelligence for Adult and Higher Education 

Decision Intelligence is an unprecedented innovation for adult and higher education that will leverage AI to deliver highly personalized human support that fosters student momentum and completion, with positive impacts particularly among students with the most friction in their education journeys, many of whom are part of traditionally underserved populations. 

Invented by Lorien Pratt and Mark Zangari, DI is defined as “a methodology and set of processes and technologies for making better, more evidence-based decisions by helping decision makers understand how the actions they take today can affect their desired outcomes in the future” (Lorien & Malcolm, 2023, p. 8).  

DI represents a paradigm shift in decision making. It can be applied at any point in a process and can replace expensive experimentation in production environments with simulations that predict the likely outcomes of different choices at key process moments. An example might be how to best route a supply of parts needed in multiple locations around the world, in the fastest way, at the least cost, arriving intact, and following all associated regulations. Simulation, predictive models, digital twins, and decision options are the key features of DI, which is much like training pilots in a simulator before having them fly the actual plane, and well before doing so with passengers. 

Decisions that are highly matrixed benefit most from the application of DI due to the exponential effect of complexity that introduces many permutations. For example, at WGU we provide individual support to students at key moments in their program journeys, such as when they first enroll and are setting term registration. The decisions to be made in that process involve many factors, including the program requirements; the student’s interests, goals, readiness and engagement; transferred courses; and more. The selection and sequence of courses is a highly complex decision that currently involves following “standard paths” that have been found to be associated with better outcomes for more students than non-standard paths, but that might be less than optimal for any individual student. Decision Intelligence can be used to make a recommendation, or provide several recommendations, from which the student and their mentor can choose and increase the likelihood of success, even enabling the student to accelerate through the program. 

We apply the protective and cost-saving benefits of DI to identify and test decision options (using simulations to predict the best outcomes for students) and provide those options to the faculty member who interacts with the student. This human-in-the-loop design serves the purposes of increasing student/faculty contact, reducing administrative process work for faculty, and leveraging simulations that reduce cost and time otherwise spent on lengthy educational experiments. Decision Intelligence speeds the delivery of personalized solutions to students, which is particularly beneficial to students who have excessive friction in their education pathways.

Reducing Friction on the Pathway to Opportunity 

Students face varied educational barriers, such as working multiple jobs, raising children while attending college, lower-quality primary education in critical topics such as math and writing, complex institutional processes and policies built into the educational journey, and more. Students with lower educational momentum caused by such sources of friction tend to drop out at higher rates, often with debt but no degree. This hazardous outcome occurs more frequently among students in lower-income situations, which is experienced more often by students who are part of traditionally underserved Black/African American, Hispanic/LatinX, or Native American populations.

Many post-secondary students do not start out at age seven with a shiny bicycle and a cartoon character helmet. In fact, many WGU students are parents riding a bus with their kids to their second job, stopping for groceries, dropping the kids at the babysitter’s (that consumes most of their paycheck), using their phone to write a paper while keeping an eye on their children and watching for their stop.    

The reasons for this disparity are beyond the scope of this post. What is pertinent here is the fact that at WGU we have found, through our model of individualized support, that students with higher amounts of friction show the greatest benefit from the addition of timely and personalized faculty outreach, which tells us that Decision Intelligence is a major play to create efficiency in our individualized support model and close the outcome gaps among sub-groups of students. All WGU students will benefit, particularly those with greater sources of friction.  

Through WGU’s unique one-to-one support model, our students thrive on their individual learning journeys, reaching their goals, advancing their careers, and caring for their families. So, while many of our adult learners may have missed the opportunity to learn how to ride a bike on their terms, we are working toward a world where their children will get that shiny bicycle with the cartoon-character helmet and a chance to learn in the way that suits them best.